code: def __init__(
      self, input_size, output_size, bias=True, activation=None
  ):
      super(NonLinear, self).__init__()
  
      self.activation = activation
      self.linear = nn.Linear(
          int(input_size), int(output_size), bias=bias
      )
  
imports: import logging
  import os
  from typing import List
  import numpy
  import torch
  import torch.nn
  from pythae.data.preprocessors import DataProcessor
  from pythae.models import VAMP, AutoModel, VAMPConfig
  from pythae.models.base.base_utils import ModelOutput
  from pythae.models.nn import BaseDecoder, BaseEncoder
  from pythae.trainers import BaseTrainer, BaseTrainerConfig
label_desc: Use image node_id 1 to create a new NonLinear object from inherited base classes: nn with example: obj = NonLinear(input_size, output_size, bias, activation)